Climatological evaluation of circulation classifications

from the COST733 database based on the Kolmogorov-Smirnov test

Radan HUTH, Monika CAHYNOVÁInstitute of Atmospheric Physics,

Prague, Czech Republichuth@ufa.cas.cz

What’s new from my last presentation

• v2.0 of the database• all domains• DJF and JJA• both temperature variables (Tmax &

Tmin)• precipitation

Which of my intentions haven’t been fulfilled

• analysis in a gridded dataset• i.e., station data are only analyzed

GOAL• assess the synoptic-climatological

applicability of classifications• i.e., how well they stratify surface weather

(climate) conditions• demonstrate effect of

– selection of the classification method– number of types– sequencing– adding more variables

• 500 hPa height • 500 hPa vorticity• 850/500 hPa thickness

– seasonality of definition

ANALYSIS

• variables– maximum temperature– minimum temperature– precipitation

• 126 stations from ECA&D database• winter (DJF), summer (JJA)• Jan 1961 – Dec 2000

TOOL• 2-sample Kolmogorov-Smirnov test• equality of distributions of the climate

element under one type against under all the other types

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TOOL

• at each station• types for which the K-S test rejects the

equality of distributions are counted• the larger the count, the better the

stratification, the better the synoptic-climatological applicability

RANKING OF CLASS’S

• at all stations individually: – for each classification: number of rejected K-S

counted– classifications ranked by the %age of rejected

K-S tests (= well separated classes)– higher %age better lower rank

• for each classification: ranks averaged over stations

• area mean rank ranking of the classification

Classifications examined

• 423 class’s in each domain are ranked• only a subset of class’s enter the analysis• omitted are

– subjective class’s & their objectivized versions– original class’s provided by authors (those with ‘o’

in the name)– WLK method– SOM method

• 367 class’s enter the competition

Classifications examined• 4 methods with 3 classifications, differing in

– number of types (9, 18, 27)• 6 methods with 30 classifications, differing in

– sequencing (no x 4 days)– additional variables (Z500, THICK850/500,

VOR500, all together)– number of types (9, 18, 27)

• 5 methods with 35 classifications, differing in – as above– seasonal definition

• infrequent types (frequency < 10 days in the given season) are omitted

Result 1: Ranking of methods

• area mean ranks averaged over 3 realizations with different numbers of types (~9, ~18, ~27) of each of 15 methods

• result: order of the method, independent of the number of types

Result 1: comparison of methods

so the winner is…

Result 1: comparison of methods

… there’s no clear winner

• ranking of methods differs– between variables– between domains– between seasons

0 4 8 12 16

GWT JCT LIT

KRZ PXE PCT PTT LND KIR

ERP CKM CAP PXK SAN RAC

0 4 8 12 16

GWT JCT LIT

KRZ PXE PCT PTT LND KIR

ERP CKM CAP PXK SAN RAC

0 4 8 12 16

GWT JCT LIT

KRZ PXE PCT PTT LND KIR

ERP CKM CAP PXK SAN RAC

0 4 8 12 16

GWT JCT LIT

KRZ PXE PCT PTT LND KIR

ERP CKM CAP PXK SAN RAC

0 4 8 12 16

GWT JCT LIT

KRZ PXE PCT PTT LND KIR

ERP CKM CAP PXK SAN RAC

DJF

0 4 8 12 16

GWT JCT LIT

KRZ PXE PCT PTT LND KIR

ERP CKM CAP PXK SAN RAC

JJA

Variables: ranks averaged over domains

0 4 8 12 16

GWT JCT LIT

KRZ PXE PCT PTT LND KIR

ERP CKM CAP PXK SAN RAC

DJF

0 4 8 12 16

GWT JCT LIT

KRZ PXE PCT PTT LND KIR

ERP CKM CAP PXK SAN RAC

JJA

ranks of Tmin, Tmax close to each other precip somewhat different

larger spread of ranks, differences even between Tmin x Tmax pattern more chaotic firm conclusions hard to draw

Domains: ranks averaged over variables

0 4 8 12 16

GWT JCT LIT

KRZ PXE PCT PTT LND KIR

ERP CKM CAP PXK SAN RAC

JJA

0 4 8 12 16

GWT JCT LIT

KRZ PXE PCT PTT LND KIR

ERP CKM CAP PXK SAN RAC

DJF

• no apparent geographical dependence• sensitivity to the size of the domain

• sensitivity to the domain size apparent for more class’s• some regional dependence

Overall rankingmethod DJF JJA

GWT 2 6 JCT 8 12 LIT 3 4 KRZ 7 8 PXE 11 5 PCT 14 13 PTT 10 15 LND 15 11 KIR 9 14 ERP 13 10 CKM 1 1 CAP 5 3 PXK 12 2 SAN 4 9 RAC 6 8

Short summary

• rankings vary among target variables, across domains, between seasons

• caution: results are contaminated (potentially biased) by unequal numbers of really occurring (enough populated) types; the contamination is stronger in JJA

• several well-performing methods can be identified: CKM, CAP, LIT, GWT

• several methods cannot be recommended: PCT, PTT, LND, KIR, ERP

Result 2: effect of sequencing

• all pairs of classifications– differing in sequencing (no vs. 4-days)– with all other attributes equal

• difference in rank is calculated• histogram of differences• t-test: equality of the difference to zero

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EFFECT OF SEQUENCING, DJF

TX

TN

RR

D00 D03 D07 D09

-72 11

-99 11

+14 9

-36 12

-55 11

+122 13

-29 13

-65 13

-12 14

+89 12

-83 13

+63 14

Effect of sequencingDJF JJA

domain TX TN RR TX TN RR00 -72.1 -99.3 +14.1 -75.5 -118.8 +41.401 +4.7 -35.2 +83.0 +40.5 -29.0 +107.102 -59.0 -75.3 +113.0 -13.8 -77.8 +94.703 -35.9 -54.6 +121.7 +2.3 -9.5 +88.804 -6.0 -69.4 +94.1 -1.3 -76.5 +90.505 -21.2 -40.5 +113.0 +6.9 -40.3 +82.906 -33.7 -58.7 +103.3 +30.1 -34.4 +108.007 -29.5 -65.1 +88.9 +2.0 -37.4 +102.008 -34.4 -59.9 +80.9 +9.1 -9.4 +99.909 -11.6 -83.2 +62.7 +22.7 -78.8 +79.310 -29.2 -53.8 +72.2 -8.9 -41.1 +92.411 -14.3 -37.4 +96.9 +42.2 -30.4 +124.7

• improves stratification for temperature• improvement larger for Tmin• deteriorates it for precip• improvement is largest / deterioration smallest for the large domain

• positive effect on temperature is weaker & less ubiquitous• deterioration for precip similar to DJF• same effect for D00 as in DJF

Result 3: sensitivity to the number of types

• all pairs of classifications– differing in no. of types

• 9 vs. 18• 18 vs. 27

– with all other attributes equal• difference in rank is calculated• histogram of differences• t-test: equality of the difference to zero

Result 3: sensitivity to the number of types

• significantly better results for lower nos. of types in almost all cases

• the only few (=3) exceptions, all for JJA and for the comparison of 9 vs. 18 types

Result 4: effect of additional variables

DJF 500 hPa height 1000/500 hPa thickness 500 hPa vorticitydomain TX TN RR TX TN RR TX TN RR

00 -56.0 -58.4 -77.7 -69.5 -73.7 -76.9 +12.3 -14.6 +11.501 -31.7 -39.6 -8.4 -34.5 -45.7 +10.8 -2.5 -22.4 +19.102 -29.7 -52.3 -16.4 -30.5 -47.5 +17.9 +13.7 +11.3 +2.703 -55.0 -60.3 -32.5 -28.3 -28.2 +3.4 +29.2 +34.0 +22.804 +8.3 +23.2 -10.4 -7.7 +.6 -7.7 +25.5 +33.1 -9.405 -63.1 -54.1 +4.6 -13.5 -9.6 +28.8 +29.9 +41.2 +47.706 -67.8 -66.6 -19.9 -29.2 -34.9 -16.8 +10.0 +30.4 +5.807 -15.9 -29.3 -2.3 +3.7 -28.0 +8.1 +34.9 +25.7 +10.508 -77.7 -89.4 -30.1 -66.9 -85.8 -23.0 +32.8 +21.1 +22.909 -72.0 -34.4 -25.0 -38.1 -10.3 -2.1 -23.9 +28.6 +26.010 -76.0 -92.5 -28.1 -39.2 -70.3 -20.5 +27.0 +28.2 +11.511 -75.8 -78.1 -82.2 -62.0 -67.1 -61.0 -6.3 +15.1 +13.2

adding height or thickness:• improvement for temperature –though spatially variable• varied response for precip• height more effective than thickness

adding vorticity:• general deterioration

Result 4: effect of additional variables

JJA 500 hPa height 1000/500 hPa thickness 500 hPa vorticitydomain TX TN RR TX TN RR TX TN RR

00 -60.5 -78.4 -36.6 -42.4 -54.1 +19.6 -5.5 -.7 -4.801 +31.3 -74.9 +71.5 +61.1 -33.9 +87.4 +10.5 -14.9 +47.002 -36.2 -98.2 +86.7 -5.6 -88.9 +92.8 -33.6 -27.4 +1.803 -48.1 -107.9 +51.9 -17.2 -85.8 +52.3 -66.2 -50.6 +20.104 -19.9 -51.8 +56.5 +29.2 -53.3 +107.0 +15.5 +14.2 +4.205 -65.1 -124.6 +67.2 -12.2 -90.9 +79.5 -38.2 -16.9 +12.006 -151.0 -183.6 -16.3 -85.5 -148.7 +28.6 -61.8 -29.9 -1.507 -119.0 -157.7 -12.8 -96.4 -136.3 +12.2 -44.7 -22.7 -18.508 -137.1 -141.4 -16.5 -109.2 -136.9 +14.7 -37.0 -7.5 -7.509 -83.3 -126.1 -123.6 -80.3 -132.5 -87.3 -8.3 -.8 -42.610 -162.6 -172.5 -27.5 -148.2 -172.5 -27.5 -42.0 -14.7 -11.711 -104.6 -92.2 -61.5 -99.4 -101.4 -35.0 -21.5 -5.7 -17.7

adding height or thickness:• improvement for temperature; stronger in SE half of Europe; stronger for Tmin• deterioration / improvement for precip in N+NW / S+SE Europe

adding vorticity:• improvement for temperature in most domains• varied response for precip

Result 5: Effect of seasonality

DJF JJAdomain TX TN RR TX TN RR

00 -44.0 -71.2 -46.4 -132.7 -94.5 -156.401 -40.6 -28.2 -17.1 -44.2 -54.7 -92.602 -75.2 -63.5 -35.1 -46.5 -14.6 -87.003 -15.5 -25.7 -55.4 +3.4 -17.0 -58.104 -37.8 -45.3 -36.2 -62.2 -49.4 -20.105 -42.5 -32.6 -21.9 -14.9 -29.0 -58.806 -26.1 -35.8 -30.5 -12.3 +18.6 -9.407 -27.1 -39.4 -28.9 -52.5 -39.5 -55.008 -36.8 -47.5 -19.6 -44.7 -57.3 -40.109 -38.1 -56.4 -61.6 -90.7 -84.0 -15.710 -16.9 -25.7 -25.5 -70.7 -64.5 -45.111 -44.9 -45.5 -17.0 -18.8 +36.9 +8.2

• general improvement• some geographical variability

BUT:systematic difference in the no. of types (7 seasonal vs. 9 non-seasonal) improvement may be partially an artifact of this difference

instead of CONCLUSIONS

• what else might have been done• or has been done by someone else and

might be nice to be combined with this study

• gridded dataset (Ensembles, NCEP or ERA40)

• other criteria of stratification